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Arizona State University - Continuous Improvement Resources at Arizona State University
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Fishbone Diagram
Purpose
To identify all of the possible factors that contribute to a problem, i.e. the "effect."
Guidelines
1. Clearly describe the problem, i.e. the "effect," to be diagrammed. (For example: files out of place, too many students in line, or job cost above estimate.)
Draw a box around the effect with an arrow heading to it.
2. Identify the major categories of factors that contribute to the problem. This will help the team organize the causes.
Four often-used categories are people, equipment, methods and materials. These categories are only suggestions. The team may use any category that helps them think creatively.
Draw a box around each category with an arrow pointing at the effect arrows.
3. Brainstorm the detailed factors that contribute to the problem (i.e. the "effect"). Ask for each factor, "what causes this cause (i.e. factor)?" These are written on the diagram and connected to the appropriate main category with arrows.
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4. Each cause may have sub-causes, which should be shown on the diagram. Continue to ask "why" in order to identify root causes.
Use the following criteria to evaluate your Fishbone Diagram:
1. Is the effect clearly stated? Does it relate to the issue statement? 2. Are all potential causes listed? 3. Are all causes categorized? 4. Do causes actually reflect causes, not solutions? 5. Do all causes relate to the issue? 6. Is the diagram complete and understandable?
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Arizona State University - Continuous Improvement Resources at Arizona State University
ASU Home | Index | Directory | Calendar | Contact ASU | Campuses: Main West East Extended
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Continuous Improvement Resources at Arizona
State University Workshops and Seminars
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Award Programs
Contact Information
Pareto Chart
Purpose
To help set priorities among a number of problems or a number of causes by arranging data graphically in descending order of frequency.
Guidelines
Construct a Pareto Chart in the following manner:
1. Separate the collected data into categories, i.e. common themes. In the case of the stakeholder interviews, list by subject or type of complaint. Other data can be categorized by shift, department or type of error.
2. Calculate the number of complaints in each category as well as the percentage of the total. You can create an "other" or "miscellaneous" category for the lowest numbered complaints.
3. Draw the bottom axis and list categories in descending order (largest to smallest). 4. Draw a vertical axis on the left side and number it, starting at zero and ending at the sum of the
responses of all of the categories. 5. Draw bars for each category, each as long as the number of responses in that category.
Below is an example of the above construction of steps 1-5 of a Pareto Chart.
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Arizona State University - Continuous Improvement Resources at Arizona State University
6. On a vertical axis on the right side, write 100% across from the sum of all of the responses and mark some intermediate percentages, e.g. 25%, 50%, 75%. Place the cumulative percentage of the total for each category above its bar and next to a dot refernced to the vertical axis (0% to 100%). For example, if the first bar is 26% of total complaints, and the second is 24% of total complaints, the dot for the second bar will be at 50%. Connect the dots with a curved line.
Next, draw a vertical line at roughly 80% (see below). This line indicates, according to the Pareto Principle that categories Not Timely, Status Unclear, High Cost, and Poor Quality account for about 80% of all stakeholder complaints. If the team puts its efforts into these categories, they will get the greatest return for their effort.
7. Title the graph and list the date of completion or time period.
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Arizona State University - Continuous Improvement Resources at Arizona State University
ASU Home | Index | Directory | Calendar | Contact ASU | Campuses: Main West East Extended
Search:
Continuous Improvement Resources at
Arizona State University Workshops and Seminars
Resources
Related Sites
Award Programs
Contact Information
Histogram
Purpose
A histogram takes measurement data, e.g. average waiting time or response time, and displays its distribution. A histogram reveals the amount of variation within any process.
A histogram is a vertical bar graph that shows the nature and distribution of data. The team can use histograms to identify problems and opportunities for improvements; to compare one process (or group, department, etc.) to another; and/or to measure if the process conforms to a certain standard. A histogram shows the information from a check sheet in visual form.
Guidelines
1. Decide if the categories on the check sheet need to be grouped. For example, if the team is counting number of days, the data might be grouped into 5-day increments (0-5 days, 6-10 days, etc.). Be careful about dividing the data into too few or too many groups.
2. Total the occurrences for the new groups. 3. Label the bottom of the histogram with the groups, starting on the left with zero or
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Arizona State University - Continuous Improvement Resources at Arizona State University
least. 4. Label the left-hand axis numerically. Start with zero at the bottom. The top number
must be at least as large as the highest group total. 5. For each group, draw a bar as tall as the number of occurrences in that group. 6. Label each axis. Add a title and a date.
Histograms have different types of distributions:
This shape occurs most often. Most of the occurrences are in the center with about an equal number on each side. This shape is the result of a normal, in control process.
A normal distribution with small variability suggests that the process is probably operating within specification limits. Measures are tightly grouped around the center.
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Arizona State University - Continuous Improvement Resources at Arizona State University
A normal distribution with with large variability suggests that the process is probably operating outside of specification limits. Measures are spread out from the center.
A skewed distribution indicates that the process is operating near an imposed limit.
The comb-like distribution may be the result of a measurement error (for example, if more than one person is recording the data or more than one instrument is used). Additional data collection may be necessary.
The bimodal distribution may be the result of measuring two or more processes with different averages together. It may be necessary to separate the data and draw a histogram for each process.
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